Cell-based methods for the detection of biomarkers in biological samples, such as blood samples, are important for many applications, including medical diagnostics, disease monitoring and prognosis, and drug discovery. With the current growth and future potential of personalized medicine, there is an increasing demand for rapid, high-throughput and sensitive methods to detect a large number of disease- and individual-specific biomarkers in order to provide personalized diagnoses and therapies to patients. However, most cell-based methods are limited in their multiplexing capabilities, speed, resolution and/or sensitivity.
Flow cytometry, such as fluorescence-activated cell sorting (FACS), is a well-known method for classifying cells and detecting biomarkers based on optical properties of labeled cells. However, conventional flow cytometry is limited in the number of labels that can be used simultaneously because of the spectral overlap between different labels. Additionally, these conventional light-based cytometric methods lack the resolution to provide subcellular distribution of biomarkers—a property that could be related to their activation state or function.
As an alternative to detecting optical signals from a sample, methods to detect molecular mass signatures of a sample using mass spectrometry are known. For example, inductively coupled plasma mass spectrometry (ICPMS) has been used to perform single-cell analysis of a sample by spraying single-cell droplets into an inductively coupled argon plasma to vaporize each cell and ionize the atomic constituents (Bendall et al., Science 2011 332:687). However, ICPMS-based methods are limited in speed, sensitivity, recovery of samples, and inherently does not reveal information related to subcellular localization of labeled targets.